Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations5121
Missing cells12098
Missing cells (%)15.7%
Total size in memory600.2 KiB
Average record size in memory120.0 B

Variable types

Text14
Numeric1

Alerts

datacommons_id has 1792 (35.0%) missing values Missing
locality_code has 5109 (99.8%) missing values Missing
locality_name has 5109 (99.8%) missing values Missing
aggregation_level is highly skewed (γ1 = 20.59127781) Skewed
location_key has unique values Unique

Reproduction

Analysis started2025-02-23 15:00:51.414308
Analysis finished2025-02-23 15:00:52.042035
Duration0.63 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

location_key
Text

Unique 

Distinct5121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
2025-02-23T16:00:52.459650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.94317516
Min length8

Characters and Unicode

Total characters56040
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5121 ?
Unique (%)100.0%

Sample

1st rowDE_BB_12051
2nd rowDE_BB_12052
3rd rowDE_BB_12053
4th rowDE_BB_12054
5th rowDE_BB_12060
ValueCountFrequency (%)
de_bb_12064 1
 
< 0.1%
us_wv_54093 1
 
< 0.1%
us_wv_54095 1
 
< 0.1%
us_wv_54097 1
 
< 0.1%
us_wv_54099 1
 
< 0.1%
us_wv_54101 1
 
< 0.1%
us_wv_54103 1
 
< 0.1%
us_wv_54105 1
 
< 0.1%
us_wv_54107 1
 
< 0.1%
us_wv_54109 1
 
< 0.1%
Other values (5111) 5111
99.8%
2025-02-23T16:00:52.864050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 10242
18.3%
S 4969
 
8.9%
0 4832
 
8.6%
1 4529
 
8.1%
U 3263
 
5.8%
2 2821
 
5.0%
3 2605
 
4.6%
5 2302
 
4.1%
7 2052
 
3.7%
E 1953
 
3.5%
Other values (27) 16472
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 10242
18.3%
S 4969
 
8.9%
0 4832
 
8.6%
1 4529
 
8.1%
U 3263
 
5.8%
2 2821
 
5.0%
3 2605
 
4.6%
5 2302
 
4.1%
7 2052
 
3.7%
E 1953
 
3.5%
Other values (27) 16472
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 10242
18.3%
S 4969
 
8.9%
0 4832
 
8.6%
1 4529
 
8.1%
U 3263
 
5.8%
2 2821
 
5.0%
3 2605
 
4.6%
5 2302
 
4.1%
7 2052
 
3.7%
E 1953
 
3.5%
Other values (27) 16472
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 10242
18.3%
S 4969
 
8.9%
0 4832
 
8.6%
1 4529
 
8.1%
U 3263
 
5.8%
2 2821
 
5.0%
3 2605
 
4.6%
5 2302
 
4.1%
7 2052
 
3.7%
E 1953
 
3.5%
Other values (27) 16472
29.4%
Distinct5079
Distinct (%)> 99.9%
Missing41
Missing (%)0.8%
Memory size40.1 KiB
2025-02-23T16:00:53.251260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters137160
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5078 ?
Unique (%)> 99.9%

Sample

1st rowChIJN8I30-XAqEcRhUxEEOyL_kg
2nd rowChIJX0qVWUJ0CEcROq1_4LUv1FA
3rd rowChIJb_u1AiqYB0cRwDteW0YgIQQ
4th rowChIJt9Y6hM31qEcRm-yqC5j4ZcU
5th rowChIJuRSkBF66qUcRCDglm8hflWE
ValueCountFrequency (%)
chij5tcocraypbircmzhtz37seq 2
 
< 0.1%
chij3styuhaacecrirhunlsslgc 1
 
< 0.1%
chijtuu0ylenb0cr3_w_qw_ineq 1
 
< 0.1%
chijy4xdqce3qucrkt7cnqbuloa 1
 
< 0.1%
chijwriqvfwrqecrm8p0zhtxuys 1
 
< 0.1%
chiju1436wdrrkcrusbulcg_aga 1
 
< 0.1%
chij4bz4zl12cecrafdesxrch78 1
 
< 0.1%
chij2qr06xbwqecrmh5rrzoh8vy 1
 
< 0.1%
chijjyvqja6jqucr9bc4ax_iag8 1
 
< 0.1%
chijauk8it1rqecrwktfw0ygiqu 1
 
< 0.1%
Other values (5069) 5069
99.8%
2025-02-23T16:00:53.584691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 8734
 
6.4%
R 6860
 
5.0%
h 6779
 
4.9%
C 6482
 
4.7%
J 6427
 
4.7%
c 3085
 
2.2%
Y 2937
 
2.1%
g 2900
 
2.1%
4 2588
 
1.9%
Q 2588
 
1.9%
Other values (54) 87780
64.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 137160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 8734
 
6.4%
R 6860
 
5.0%
h 6779
 
4.9%
C 6482
 
4.7%
J 6427
 
4.7%
c 3085
 
2.2%
Y 2937
 
2.1%
g 2900
 
2.1%
4 2588
 
1.9%
Q 2588
 
1.9%
Other values (54) 87780
64.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 137160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 8734
 
6.4%
R 6860
 
5.0%
h 6779
 
4.9%
C 6482
 
4.7%
J 6427
 
4.7%
c 3085
 
2.2%
Y 2937
 
2.1%
g 2900
 
2.1%
4 2588
 
1.9%
Q 2588
 
1.9%
Other values (54) 87780
64.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 137160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 8734
 
6.4%
R 6860
 
5.0%
h 6779
 
4.9%
C 6482
 
4.7%
J 6427
 
4.7%
c 3085
 
2.2%
Y 2937
 
2.1%
g 2900
 
2.1%
4 2588
 
1.9%
Q 2588
 
1.9%
Other values (54) 87780
64.0%
Distinct5097
Distinct (%)> 99.9%
Missing23
Missing (%)0.4%
Memory size40.1 KiB
2025-02-23T16:00:53.971033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.766378972
Min length3

Characters and Unicode

Total characters34495
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5096 ?
Unique (%)> 99.9%

Sample

1st rowQ3931
2nd rowQ3214
3rd rowQ4024
4th rowQ1711
5th rowQ6115
ValueCountFrequency (%)
q1492 2
 
< 0.1%
q6109 1
 
< 0.1%
q115413 1
 
< 0.1%
q4024 1
 
< 0.1%
q8520 1
 
< 0.1%
q6115 1
 
< 0.1%
q6173 1
 
< 0.1%
q6165 1
 
< 0.1%
q6139 1
 
< 0.1%
q3931 1
 
< 0.1%
Other values (5087) 5087
99.8%
2025-02-23T16:00:54.500007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Q 5098
14.8%
1 4325
12.5%
4 3637
10.5%
5 3184
9.2%
9 2863
8.3%
8 2775
8.0%
6 2745
8.0%
0 2691
7.8%
2 2495
7.2%
3 2371
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 5098
14.8%
1 4325
12.5%
4 3637
10.5%
5 3184
9.2%
9 2863
8.3%
8 2775
8.0%
6 2745
8.0%
0 2691
7.8%
2 2495
7.2%
3 2371
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 5098
14.8%
1 4325
12.5%
4 3637
10.5%
5 3184
9.2%
9 2863
8.3%
8 2775
8.0%
6 2745
8.0%
0 2691
7.8%
2 2495
7.2%
3 2371
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 5098
14.8%
1 4325
12.5%
4 3637
10.5%
5 3184
9.2%
9 2863
8.3%
8 2775
8.0%
6 2745
8.0%
0 2691
7.8%
2 2495
7.2%
3 2371
6.9%

datacommons_id
Text

Missing 

Distinct3329
Distinct (%)100.0%
Missing1792
Missing (%)35.0%
Memory size40.1 KiB
2025-02-23T16:00:54.796607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.93872034
Min length9

Characters and Unicode

Total characters36415
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3329 ?
Unique (%)100.0%

Sample

1st rowiso/IT-AL
2nd rowiso/IT-AT
3rd rowiso/IT-BI
4th rowiso/IT-CN
5th rowiso/IT-NO
ValueCountFrequency (%)
iso/it-al 1
 
< 0.1%
geoid/56031 1
 
< 0.1%
geoid/56033 1
 
< 0.1%
geoid/56035 1
 
< 0.1%
geoid/56037 1
 
< 0.1%
geoid/56039 1
 
< 0.1%
geoid/56041 1
 
< 0.1%
geoid/56043 1
 
< 0.1%
geoid/56045 1
 
< 0.1%
geoid/56015 1
 
< 0.1%
Other values (3319) 3319
99.7%
2025-02-23T16:00:55.282806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 3336
9.2%
o 3329
9.1%
/ 3329
9.1%
e 3226
8.9%
g 3226
8.9%
d 3226
8.9%
1 3049
8.4%
0 3044
8.4%
3 1802
 
4.9%
2 1572
 
4.3%
Other values (30) 7276
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 3336
9.2%
o 3329
9.1%
/ 3329
9.1%
e 3226
8.9%
g 3226
8.9%
d 3226
8.9%
1 3049
8.4%
0 3044
8.4%
3 1802
 
4.9%
2 1572
 
4.3%
Other values (30) 7276
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 3336
9.2%
o 3329
9.1%
/ 3329
9.1%
e 3226
8.9%
g 3226
8.9%
d 3226
8.9%
1 3049
8.4%
0 3044
8.4%
3 1802
 
4.9%
2 1572
 
4.3%
Other values (30) 7276
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 3336
9.2%
o 3329
9.1%
/ 3329
9.1%
e 3226
8.9%
g 3226
8.9%
d 3226
8.9%
1 3049
8.4%
0 3044
8.4%
3 1802
 
4.9%
2 1572
 
4.3%
Other values (30) 7276
20.0%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
2025-02-23T16:00:55.383136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10242
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDE
2nd rowDE
3rd rowDE
4th rowDE
5th rowDE
ValueCountFrequency (%)
us 3228
63.0%
es 1378
26.9%
de 412
 
8.0%
it 103
 
2.0%
2025-02-23T16:00:55.593316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 4606
45.0%
U 3228
31.5%
E 1790
 
17.5%
D 412
 
4.0%
I 103
 
1.0%
T 103
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4606
45.0%
U 3228
31.5%
E 1790
 
17.5%
D 412
 
4.0%
I 103
 
1.0%
T 103
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4606
45.0%
U 3228
31.5%
E 1790
 
17.5%
D 412
 
4.0%
I 103
 
1.0%
T 103
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4606
45.0%
U 3228
31.5%
E 1790
 
17.5%
D 412
 
4.0%
I 103
 
1.0%
T 103
 
1.0%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
2025-02-23T16:00:55.795615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length24
Mean length17.13747315
Min length5

Characters and Unicode

Total characters87761
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowGermany
3rd rowGermany
4th rowGermany
5th rowGermany
ValueCountFrequency (%)
united 3228
21.8%
states 3228
21.8%
of 3228
21.8%
america 3228
21.8%
spain 1378
9.3%
germany 412
 
2.8%
italy 103
 
0.7%
2025-02-23T16:00:56.126595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10096
11.5%
t 9787
11.2%
9684
11.0%
a 8349
 
9.5%
i 7834
 
8.9%
n 5018
 
5.7%
S 4606
 
5.2%
r 3640
 
4.1%
m 3640
 
4.1%
U 3228
 
3.7%
Other values (11) 21879
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10096
11.5%
t 9787
11.2%
9684
11.0%
a 8349
 
9.5%
i 7834
 
8.9%
n 5018
 
5.7%
S 4606
 
5.2%
r 3640
 
4.1%
m 3640
 
4.1%
U 3228
 
3.7%
Other values (11) 21879
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10096
11.5%
t 9787
11.2%
9684
11.0%
a 8349
 
9.5%
i 7834
 
8.9%
n 5018
 
5.7%
S 4606
 
5.2%
r 3640
 
4.1%
m 3640
 
4.1%
U 3228
 
3.7%
Other values (11) 21879
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10096
11.5%
t 9787
11.2%
9684
11.0%
a 8349
 
9.5%
i 7834
 
8.9%
n 5018
 
5.7%
S 4606
 
5.2%
r 3640
 
4.1%
m 3640
 
4.1%
U 3228
 
3.7%
Other values (11) 21879
24.9%
Distinct89
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
2025-02-23T16:00:56.317975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10242
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB
ValueCountFrequency (%)
ct 1091
21.3%
tx 254
 
5.0%
md 224
 
4.4%
ga 160
 
3.1%
va 133
 
2.6%
ky 120
 
2.3%
mo 115
 
2.2%
ks 105
 
2.1%
il 102
 
2.0%
nc 100
 
2.0%
Other values (79) 2717
53.1%
2025-02-23T16:00:56.613411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1576
15.4%
C 1456
14.2%
N 870
 
8.5%
A 821
 
8.0%
M 720
 
7.0%
I 550
 
5.4%
D 391
 
3.8%
O 380
 
3.7%
S 347
 
3.4%
K 331
 
3.2%
Other values (23) 2800
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1576
15.4%
C 1456
14.2%
N 870
 
8.5%
A 821
 
8.0%
M 720
 
7.0%
I 550
 
5.4%
D 391
 
3.8%
O 380
 
3.7%
S 347
 
3.4%
K 331
 
3.2%
Other values (23) 2800
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1576
15.4%
C 1456
14.2%
N 870
 
8.5%
A 821
 
8.0%
M 720
 
7.0%
I 550
 
5.4%
D 391
 
3.8%
O 380
 
3.7%
S 347
 
3.4%
K 331
 
3.2%
Other values (23) 2800
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1576
15.4%
C 1456
14.2%
N 870
 
8.5%
A 821
 
8.0%
M 720
 
7.0%
I 550
 
5.4%
D 391
 
3.8%
O 380
 
3.7%
S 347
 
3.4%
K 331
 
3.2%
Other values (23) 2800
27.3%
Distinct91
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
2025-02-23T16:00:56.922302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length22
Mean length9.024409295
Min length4

Characters and Unicode

Total characters46214
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowBrandenburg
2nd rowBrandenburg
3rd rowBrandenburg
4th rowBrandenburg
5th rowBrandenburg
ValueCountFrequency (%)
cataluña 1083
 
17.3%
texas 254
 
4.1%
north 206
 
3.3%
de 200
 
3.2%
madrid 200
 
3.2%
comunidad 200
 
3.2%
virginia 188
 
3.0%
georgia 160
 
2.6%
carolina 146
 
2.3%
new 127
 
2.0%
Other values (93) 3497
55.9%
2025-02-23T16:00:57.492529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8342
18.1%
i 3799
 
8.2%
n 2929
 
6.3%
o 2656
 
5.7%
s 2579
 
5.6%
e 2356
 
5.1%
t 2305
 
5.0%
l 2232
 
4.8%
r 2116
 
4.6%
u 1877
 
4.1%
Other values (41) 15023
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8342
18.1%
i 3799
 
8.2%
n 2929
 
6.3%
o 2656
 
5.7%
s 2579
 
5.6%
e 2356
 
5.1%
t 2305
 
5.0%
l 2232
 
4.8%
r 2116
 
4.6%
u 1877
 
4.1%
Other values (41) 15023
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8342
18.1%
i 3799
 
8.2%
n 2929
 
6.3%
o 2656
 
5.7%
s 2579
 
5.6%
e 2356
 
5.1%
t 2305
 
5.0%
l 2232
 
4.8%
r 2116
 
4.6%
u 1877
 
4.1%
Other values (41) 15023
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8342
18.1%
i 3799
 
8.2%
n 2929
 
6.3%
o 2656
 
5.7%
s 2579
 
5.6%
e 2356
 
5.1%
t 2305
 
5.0%
l 2232
 
4.8%
r 2116
 
4.6%
u 1877
 
4.1%
Other values (41) 15023
32.5%
Distinct4680
Distinct (%)91.6%
Missing12
Missing (%)0.2%
Memory size40.1 KiB
2025-02-23T16:00:57.975061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.947739284
Min length2

Characters and Unicode

Total characters25278
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4266 ?
Unique (%)83.5%

Sample

1st row12051
2nd row12052
3rd row12053
4th row12054
5th row12060
ValueCountFrequency (%)
08115 3
 
0.1%
12073 3
 
0.1%
08125 3
 
0.1%
08117 3
 
0.1%
12053 3
 
0.1%
12061 3
 
0.1%
12051 3
 
0.1%
08111 3
 
0.1%
12065 3
 
0.1%
08119 3
 
0.1%
Other values (4670) 5079
99.4%
2025-02-23T16:00:58.460324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4832
19.1%
1 4521
17.9%
2 2769
11.0%
3 2594
10.3%
5 2252
8.9%
7 2026
8.0%
4 1747
 
6.9%
8 1691
 
6.7%
9 1582
 
6.3%
6 1058
 
4.2%
Other values (22) 206
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4832
19.1%
1 4521
17.9%
2 2769
11.0%
3 2594
10.3%
5 2252
8.9%
7 2026
8.0%
4 1747
 
6.9%
8 1691
 
6.7%
9 1582
 
6.3%
6 1058
 
4.2%
Other values (22) 206
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4832
19.1%
1 4521
17.9%
2 2769
11.0%
3 2594
10.3%
5 2252
8.9%
7 2026
8.0%
4 1747
 
6.9%
8 1691
 
6.7%
9 1582
 
6.3%
6 1058
 
4.2%
Other values (22) 206
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4832
19.1%
1 4521
17.9%
2 2769
11.0%
3 2594
10.3%
5 2252
8.9%
7 2026
8.0%
4 1747
 
6.9%
8 1691
 
6.7%
9 1582
 
6.3%
6 1058
 
4.2%
Other values (22) 206
 
0.8%
Distinct3834
Distinct (%)75.0%
Missing12
Missing (%)0.2%
Memory size40.1 KiB
2025-02-23T16:00:58.965837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length33
Mean length13.22528871
Min length3

Characters and Unicode

Total characters67568
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3401 ?
Unique (%)66.6%

Sample

1st rowBrandenburg an der Havel
2nd rowCottbus
3rd rowFrankfurt an der Oder
4th rowPotsdam
5th rowBarnim
ValueCountFrequency (%)
county 3008
30.1%
de 334
 
3.3%
la 140
 
1.4%
sant 92
 
0.9%
municipio 78
 
0.8%
del 77
 
0.8%
parish 64
 
0.6%
el 50
 
0.5%
santa 42
 
0.4%
san 40
 
0.4%
Other values (3934) 6078
60.8%
2025-02-23T16:00:59.589719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 6219
 
9.2%
o 5846
 
8.7%
a 5025
 
7.4%
4894
 
7.2%
t 4838
 
7.2%
e 4496
 
6.7%
u 4228
 
6.3%
C 3691
 
5.5%
y 3444
 
5.1%
r 3194
 
4.7%
Other values (71) 21693
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 6219
 
9.2%
o 5846
 
8.7%
a 5025
 
7.4%
4894
 
7.2%
t 4838
 
7.2%
e 4496
 
6.7%
u 4228
 
6.3%
C 3691
 
5.5%
y 3444
 
5.1%
r 3194
 
4.7%
Other values (71) 21693
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 6219
 
9.2%
o 5846
 
8.7%
a 5025
 
7.4%
4894
 
7.2%
t 4838
 
7.2%
e 4496
 
6.7%
u 4228
 
6.3%
C 3691
 
5.5%
y 3444
 
5.1%
r 3194
 
4.7%
Other values (71) 21693
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 6219
 
9.2%
o 5846
 
8.7%
a 5025
 
7.4%
4894
 
7.2%
t 4838
 
7.2%
e 4496
 
6.7%
u 4228
 
6.3%
C 3691
 
5.5%
y 3444
 
5.1%
r 3194
 
4.7%
Other values (71) 21693
32.1%

locality_code
Text

Missing 

Distinct12
Distinct (%)100.0%
Missing5109
Missing (%)99.8%
Memory size40.1 KiB
2025-02-23T16:00:59.736550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)100.0%

Sample

1st rowACE
2nd rowFUE
3rd rowGMZ
4th rowLPA
5th rowSPC
ValueCountFrequency (%)
ace 1
8.3%
fue 1
8.3%
gmz 1
8.3%
lpa 1
8.3%
spc 1
8.3%
tfn 1
8.3%
vde 1
8.3%
bcn 1
8.3%
mad 1
8.3%
sfo 1
8.3%
Other values (2) 2
16.7%
2025-02-23T16:01:00.046484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4
11.1%
C 4
11.1%
E 3
 
8.3%
F 3
 
8.3%
N 3
 
8.3%
L 2
 
5.6%
P 2
 
5.6%
T 2
 
5.6%
S 2
 
5.6%
M 2
 
5.6%
Other values (8) 9
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4
11.1%
C 4
11.1%
E 3
 
8.3%
F 3
 
8.3%
N 3
 
8.3%
L 2
 
5.6%
P 2
 
5.6%
T 2
 
5.6%
S 2
 
5.6%
M 2
 
5.6%
Other values (8) 9
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4
11.1%
C 4
11.1%
E 3
 
8.3%
F 3
 
8.3%
N 3
 
8.3%
L 2
 
5.6%
P 2
 
5.6%
T 2
 
5.6%
S 2
 
5.6%
M 2
 
5.6%
Other values (8) 9
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4
11.1%
C 4
11.1%
E 3
 
8.3%
F 3
 
8.3%
N 3
 
8.3%
L 2
 
5.6%
P 2
 
5.6%
T 2
 
5.6%
S 2
 
5.6%
M 2
 
5.6%
Other values (8) 9
25.0%

locality_name
Text

Missing 

Distinct12
Distinct (%)100.0%
Missing5109
Missing (%)99.8%
Memory size40.1 KiB
2025-02-23T16:01:00.166825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.666666667
Min length6

Characters and Unicode

Total characters116
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)100.0%

Sample

1st rowLanzarote
2nd rowFuerteventura
3rd rowLa Gomera
4th rowGran Canaria
5th rowLa Palma
ValueCountFrequency (%)
la 2
 
10.5%
lanzarote 1
 
5.3%
fuerteventura 1
 
5.3%
gomera 1
 
5.3%
gran 1
 
5.3%
canaria 1
 
5.3%
palma 1
 
5.3%
tenerife 1
 
5.3%
el 1
 
5.3%
hierro 1
 
5.3%
Other values (8) 8
42.1%
2025-02-23T16:01:00.478966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 19
16.4%
r 13
11.2%
e 11
 
9.5%
n 9
 
7.8%
7
 
6.0%
t 6
 
5.2%
i 6
 
5.2%
o 6
 
5.2%
l 4
 
3.4%
L 3
 
2.6%
Other values (24) 32
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 19
16.4%
r 13
11.2%
e 11
 
9.5%
n 9
 
7.8%
7
 
6.0%
t 6
 
5.2%
i 6
 
5.2%
o 6
 
5.2%
l 4
 
3.4%
L 3
 
2.6%
Other values (24) 32
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 19
16.4%
r 13
11.2%
e 11
 
9.5%
n 9
 
7.8%
7
 
6.0%
t 6
 
5.2%
i 6
 
5.2%
o 6
 
5.2%
l 4
 
3.4%
L 3
 
2.6%
Other values (24) 32
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 19
16.4%
r 13
11.2%
e 11
 
9.5%
n 9
 
7.8%
7
 
6.0%
t 6
 
5.2%
i 6
 
5.2%
o 6
 
5.2%
l 4
 
3.4%
L 3
 
2.6%
Other values (24) 32
27.6%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
2025-02-23T16:01:00.533942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10242
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDE
2nd rowDE
3rd rowDE
4th rowDE
5th rowDE
ValueCountFrequency (%)
us 3228
63.0%
es 1378
26.9%
de 412
 
8.0%
it 103
 
2.0%
2025-02-23T16:01:00.692628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 4606
45.0%
U 3228
31.5%
E 1790
 
17.5%
D 412
 
4.0%
I 103
 
1.0%
T 103
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4606
45.0%
U 3228
31.5%
E 1790
 
17.5%
D 412
 
4.0%
I 103
 
1.0%
T 103
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4606
45.0%
U 3228
31.5%
E 1790
 
17.5%
D 412
 
4.0%
I 103
 
1.0%
T 103
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4606
45.0%
U 3228
31.5%
E 1790
 
17.5%
D 412
 
4.0%
I 103
 
1.0%
T 103
 
1.0%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
2025-02-23T16:01:00.771860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15363
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEU
2nd rowDEU
3rd rowDEU
4th rowDEU
5th rowDEU
ValueCountFrequency (%)
usa 3228
63.0%
esp 1378
26.9%
deu 412
 
8.0%
ita 103
 
2.0%
2025-02-23T16:01:00.985996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 4606
30.0%
U 3640
23.7%
A 3331
21.7%
E 1790
 
11.7%
P 1378
 
9.0%
D 412
 
2.7%
I 103
 
0.7%
T 103
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4606
30.0%
U 3640
23.7%
A 3331
21.7%
E 1790
 
11.7%
P 1378
 
9.0%
D 412
 
2.7%
I 103
 
0.7%
T 103
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4606
30.0%
U 3640
23.7%
A 3331
21.7%
E 1790
 
11.7%
P 1378
 
9.0%
D 412
 
2.7%
I 103
 
0.7%
T 103
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4606
30.0%
U 3640
23.7%
A 3331
21.7%
E 1790
 
11.7%
P 1378
 
9.0%
D 412
 
2.7%
I 103
 
0.7%
T 103
 
0.7%

aggregation_level
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.002343292
Minimum2
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2025-02-23T16:01:01.060761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum3
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04835553648
Coefficient of variation (CV)0.0241494736
Kurtosis422.1655981
Mean2.002343292
Median Absolute Deviation (MAD)0
Skewness20.59127781
Sum10254
Variance0.002338257909
MonotonicityNot monotonic
2025-02-23T16:01:01.156695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2 5109
99.8%
3 12
 
0.2%
ValueCountFrequency (%)
2 5109
99.8%
3 12
 
0.2%
ValueCountFrequency (%)
3 12
 
0.2%
2 5109
99.8%